Deep Learning in Image Analysis and Pattern Recognition, 2nd Edition

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).

Deadline for manuscript submissions: 26 November 2025 | Viewed by 1265

Special Issue Editors


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Guest Editor
School of Computer Science, University of Technology Sydney, Sydney 2007, Australia
Interests: graph mining; multimodal learning; time series analysis; recommender systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, China
Interests: machine learning; data mining; medical informatics; bioinformatics and service computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past decade, deep learning has demonstrated state-of-the-art performances in many image processing tasks, such as image classification, object detection, object tracking, image segmentation, etc. Despite remarkable deep learning-related achievements in computer vision, many challenging tasks and scenarios that require novel methods and theories remain. For example, lightweight object detection is required in many applications, such as autonomous driving scenarios; however, the discrepancy between the speed of a machine and human eyes remains large. Fine-grained or small-object detection is another area that has seen improvement. Weakly supervised object classification or detection is an important problem since the annotation process is time-consuming, expensive and inefficient. In addition, although deep learning achievements in computer vision have been successfully applied to many areas, greater efforts should be made to ensure that this technology can better serve humans in the future.

This Special Issue requests papers detailing new advances in deep learning methods or applications in image analysis and pattern recognition. Topics of interest include, but are not limited to, the following:

  • Image classification;
  • Object detection;
  • Object tracking;
  • Image segmentation;
  • Convolutional neural networks;
  • Diffusion model;
  • Image captioning;
  • Image clustering;
  • Representation learning for images;
  • Medical image processing;
  • Remote sensing image processing;
  • 3D image building.

Dr. Xianzhi Wang
Dr. Guoqing Chao
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • image analysis
  • pattern recognition

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Published Papers (1 paper)

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Research

14 pages, 1580 KiB  
Article
Machine Learning Classification of Fossilized Pectinodon bakkeri Teeth Images: Insights into Troodontid Theropod Dinosaur Morphology
by Jacob Bahn, Germán H. Alférez and Keith Snyder
Mach. Learn. Knowl. Extr. 2025, 7(2), 45; https://doi.org/10.3390/make7020045 - 21 May 2025
Viewed by 855
Abstract
Although the manual classification of microfossils is possible, it can become burdensome. Machine learning offers an alternative that allows for automatic classification. Our contribution is to use machine learning to develop an automated approach for classifying images of Pectinodon bakkeri teeth. This can [...] Read more.
Although the manual classification of microfossils is possible, it can become burdensome. Machine learning offers an alternative that allows for automatic classification. Our contribution is to use machine learning to develop an automated approach for classifying images of Pectinodon bakkeri teeth. This can be expanded for use with many other species. Our approach is composed of two steps. First, PCA and K-means were applied to a numerical dataset with 459 samples collected at the Hanson Ranch Bonebed in eastern Wyoming, containing the following features: crown height, fore-aft basal length, basal width, anterior denticles, and posterior denticles per millimeter. The results obtained in this step were used to automatically organize the P. bakkeri images from two out of three clusters generated. Finally, the tooth images were used to train a convolutional neural network with two classes. The model has an accuracy of 71%, a precision of 71%, a recall of 70.5%, and an F1-score of 70.5%. Full article
(This article belongs to the Special Issue Deep Learning in Image Analysis and Pattern Recognition, 2nd Edition)
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